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Introduction to Stress and Lifestyle01:27

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Stress is a multifaceted response to events perceived as challenging or threatening, highlighting physical, emotional, cognitive, and behavioral reactions. Physically, stress can lead to fatigue, sleep disruptions, and various health issues such as frequent colds, chest pains, and nausea. Emotionally, it can manifest as anxiety, depression, irritability, and anger triggered by both minor and major life events. Cognitively, it may result in difficulty in concentration, memory, and...
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Physiological Foundation of Stress01:24

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Stress triggers a coordinated physiological response involving the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis. This dual activation ensures that the body is prepared for both immediate and prolonged stress management. The process begins with the perception of a stressor. This initial phase activates the SNS, leading to the rapid release of adrenaline (epinephrine) from the adrenal glands.
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Psychological Responses to Stress01:20

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Psychological responses to stress encompass the various cognitive and emotional reactions individuals experience when faced with challenging or threatening situations, such as a job loss. Prolonged exposure to stressors can disturb emotional balance, increasing negative emotions (e.g., anxiety and sadness) and diminishing positive emotions (e.g., joy and satisfaction). These persistent emotional shifts are associated with an increased risk of both physical illness and mental health issues, such...
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The stress response system, also known as the fight-or-flight response, is the body's automatic physiological reaction to perceived threats. Hans Selye introduced the concept of General Adaptation Syndrome (GAS) to describe the predictable pattern of changes that occur in response to stress. GAS consists of three sequential stages: alarm, resistance, and exhaustion. This model helps explain how chronic stress can contribute to health problems.
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Applications of Stress01:04

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Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
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Stress Prevention and Stress Management Techniques I01:26

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Stress prevention and management are crucial for maintaining well-being and building resilience. Techniques to manage stress include cultivating qualities like conscientiousness, a sense of personal control, and self-efficacy. Each of these traits significantly reduces stress and promotes healthier lifestyle choices and outcomes.
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Towards a Resilience to Stress Index Based on Physiological Response: A Machine Learning Approach.

Ramon E Diaz-Ramos1, Daniela A Gomez-Cravioto1, Luis A Trejo2

  • 1Department of Computer Science, School of Engineering and Sciences, Campus Monterrey, Tecnologico de Monterrey, Monterrey 64849, Mexico.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed a new Resilience to Stress Index (RSI) using physiological data from a stress test. This index reliably measures stress resilience, offering potential benefits for mental health tracking and support.

Keywords:
clusteringmachine learningphysiological responseresilience to stress

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Area of Science:

  • Psychophysiology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Stress resilience is crucial for mental health.
  • Existing resilience measures may lack objective physiological correlates.
  • Quantifying stress resilience can aid in mental health interventions.

Purpose of the Study:

  • To propose and validate a novel index for measuring individual resilience to stress.
  • To utilize physiological variables and machine learning for resilience assessment.
  • To establish a reliable, data-driven metric for stress resilience.

Main Methods:

  • Collected physiological data (electromyography, blood volume pulse, etc.) from 71 individuals during a stress test.
  • Employed Principal Components Analysis (PCA) to visualize feature variability across test phases.
  • Applied unsupervised machine learning (Euclidean, Mahalanobis, Kernel PCA distances) to compute the Resilience to Stress Index (RSI).

Main Results:

  • Physiological features formed distinct clusters within test phases, enabling index computation.
  • The proposed RSI demonstrated reliability and was validated against the Resilience in Mexicans (RESI-M) scale.
  • Mahalanobis distance was recommended for RSI computation due to its strong association with the RESI-M scale.

Conclusions:

  • A reliable Resilience to Stress Index (RSI) can be computed using physiological data and machine learning.
  • The RSI offers a promising tool for objectively assessing and understanding stress resilience.
  • This metric has potential applications in mental health monitoring and support.